Umetrics Suite Blog

Lennart Eriksson

Lennart Eriksson
Sr Lecturer and Principal Data Scientist at Sartorius Stedim Data Analytics
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Recent Posts

Are differences in technician performance affecting your product’s quality?

October 9, 2018

In a manufacturing setting where consistent quality matters, variability in how individual technicians and operators perform their jobs can be frustrating for managers. Companies need a way to achieve consistent quality, without reducing the capacity for innovation and improvement.

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What is DOE? Design of Experiments Basics for Beginners

August 9, 2018

[This blog was a favorite last year, so we thought you'd like to see it again. Send us your comments!].

Whether you work in engineering, R&D, or a science lab, understanding the basics of experimental design can help you achieve more statistically optimal results from your experiments or improve your output quality.

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How to use data analytics to understand consumer preferences

July 12, 2018

Product development and innovation are important elements for the survival of many companies. Whether introducing a new food flavor or adding new product features, understanding consumer preferences can help guide both design and production decisions. The right decisions can make a product launch more successful, and ultimately more profitable.

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Multivariate data analysis could be used to evaluate teaching practices in schools

June 28, 2018

Formative assessment has come into focus in recent years. In Sweden, the use of formative assessment is typically emphasized in the curriculum of upper secondary schools. However, scientific studies show both positive as well as no effects at all of formative assessment on student performance.

Furthermore, formative assessment has proved to be time consuming, which obviously is a problem if it has no effects on learning. A new thesis by Daniel Larsson at the Linnæus University, Sweden, shows that multivariate data analysis, MVDA, can be used to give some answers about the effectiveness of such teaching practices.

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Can multivariate data analysis predict the winner of the World Cup?

June 13, 2018

Multivariate data analysis (MVDA) is a statistical technique that can be used to analyze data with more than one variable in order to look for deviations and understand the relationships between the different data points. In practice, this can mean taking data from a number of different sources and turning it into meaningful information from which you can draw some conclusions.

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A case for using predictive analytics to optimize and control bioprocesses

June 1, 2018

In bioprocessing today, a shift is happening that takes the ability to monitor, optimize and control processes to the next level. Whereas in the past manufacturers aspired to measure data in order to find out why a bioprocess action happened (using descriptive and diagnostic analytics), today we are able to use predictive analytics to determine what will happen in a bioprocess based on specific process data measured in real-time. This migration “up the food chain” to a higher level of data analytics requires automation, ongoing process monitoring and the ability to make adjustments in real-time.

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Using data analytics to optimize design space and setpoint conditions for bioreactors

May 18, 2018

At the heart of any process used to manufacture biological products is a bioreactor setup that supports a stable and reproducible biologically active environment. The bioreactor provides a controlled environment to achieve optimal growth for the particular cell cultures being used.

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How to optimize cell culture media to speed biopharma development

May 7, 2018

Biopharmaceutical companies today are challenged to develop high producing cell lines as quickly as possible. Commercially available media may fall short of performance expectations required to meet targets. The alternative —fully customized media and feed development — requires significant funding, time and in-house expertise in media development.

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What tools make DOE data analysis faster and more accurate?

April 19, 2018

In life science, biopharma and other areas of research, development and production, design of experiments (DOE) provides a systematic method to determine cause and effect relationships between factors and responses affecting a process, product or analytical system. But the key to understanding your results is effective analysis of your experimental data.

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What is principal component analysis (PCA) and how it is used?

April 6, 2018

Principal component analysis, or PCA, is a statistical procedure that allows you to summarize the information content in large data tables by means of a smaller set of “summary indices” that can be more easily visualized and analyzed. The underlying data can be measurements describing properties of production samples, chemical compounds or reactions, process time points of a continuous process, batches from a batch process, biological individuals or trials of a DOE-protocol, for example.

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